Molecule biomarkers of autism

ABSTRACT

This invention provides methods and biomarkers for diagnosing autism by identifying cellular metabolites differentially produced in autistic patient samples versus non-autistic controls. Methods for identifying a unique profile of metabolites present of secreted in brain tissue, cerebrospinal fluid, plasma, or biofluids of autistic samples are described herein. The individual metabolites or a pattern of secreted metabolites provide metabolic signatures of autism, which can be used to provide a diagnosis thereof.

This application claims the priority benefit of U.S. provisional patentapplication Ser. No. 61/329,515 filed Apr. 29, 2010, the entirety ofwhich is herein incorporated by reference.

FIELD OF THE INVENTION

This invention relates to autism and diagnosing autism and autismspectrum disorders. Specifically, the invention provides methods andbiomarkers for identifying individuals, particularly children, havingautism and autism spectrum disorders. More particularly, the inventionprovides methods for identifying metabolites secreted by brain tissuesand into biofluids of individuals with autism, wherein such metaboliteshave a molecular weight from about 10 Daltons to about 1500 Daltons. Theinvention provides multiple collections or spectra that comprise one ora plurality of such metabolites that are present in autistic individualsthat are diagnostically and significantly different from levels found innon-autistic individuals. Collections of one or more differentiallysecreted metabolites provided herein comprise metabolic signaturesdiagnostic for autism. Additionally, specific biomarkers for autism areidentified herein.

BACKGROUND OF THE INVENTION

Autism is a neurological disorder characterized by alterations in socialinteraction, language development, repetitive movements and patterns ofbehavior. Its prevalence has increased from 1 in 2,325 births prior tothe 1980's to an alarming 1 in 101 births today (Blaylock et al., 2009,Curr. Med. Chem. 16:157-170). Autism is a very complex neurologicaldisorder that does not follow strictly genetic or deterministic etiology(Lander et al., 1994, Science, 265:2037-48; Baron-Cohen et al., 2005,Science, 310:819-23; Santangelo et al., 2005, Am. J. Pharmacogenomic.5:71-92). Accordingly, defining a role for metabolism in thepathogenesis of autism is important to developing an understanding ofthe disorder and providing an accurate diagnosis and patient managementof the disease, because strictly genetic causes account for onlyapproximately 10% of autism cases. Multiple candidate susceptibilitygenes have been identified, such as the serotonin transporter gene(5HTT), the GABA receptor β subunit (GABRB3), ubiquitin ligase 3(UBE3A), wingless type MMTV integration site family member 2 (WNT2), andreelin (RELN) (Folstein et al., 2001, Nat. Rev. Genet. 2:943-955;Buxbaum et al., 2004, Mol. Psychiatry, 9:144-150; Devlin et al., 2005,Mol. Psych. 10:1110-1116). Despite the genetic diversity underlyingmutations and patient symptoms (McCracken et al., 2002, N. Engl. J. Med.347:314-321; Moore et al., 2004, Ann. Pharmacother. 38:1515-1519; Zhaoet al., 2005, Intern. J. Neuroscience, 115:1183-1191) there seems to bea converging metabolic dysfunction across cases (Bryson et al., 1998,Mental Retard. Dev. Disabil. Res. Rev. 4:97-103; Betancur et al., 2002,Mol. Psychiatry, 7:67-71; Moore et al., 2004, Ann. Pharmacotherapy.38:1515-19). Metabolism and metabolic alterations can play a significantrole during neurodevelopment and autism pathogenesis (Pardo et al.,2007, Brain Pathol. 17:434-447). Thus, comparative studies to examineglobal biochemical differences between autistic and non-autistic braincan identify specific metabolic pathways that contribute to autism onsetand progress. (Moore et al., 2004, Pharmacother. 38:1515-1519; Rasalamet al., 2005, Dev. Med. Child Neurol. 47 (8):551-555).

SUMMARY OF THE INVENTION

This invention provides reagents and methods for identifying a pluralityof metabolite compounds differentially produced in autistic patients, aswell as methods for using one or a plurality of such identifiedmetabolite compounds for providing a diagnosis of autism or autisticspectrum disorder. Said metabolites are found using the methods setforth herein differentially secreted in patient tissues or biofluids,particularly brain-associated biofluids such as cerebrospinal fluid.These metabolites are found in either greater or lesser amounts inautistic as compared to non-autistic individuals. Additionally, theinvention provides biomarkers, either individually or in collections ofspectra of a plurality of said biomarkers, comprising the metabolitesdifferentially detected in biofluids from autistic individuals,particularly children. Said collections or spectra are useful in thepractice of the methods of the invention for diagnosing of autism.

As set forth herein, multiple neurodevelopmental disorders includingautism can produce alterations in common metabolic pathways. It will beunderstood by those skilled in the art that metabolites (e.g., smallmolecules from about 10 Daltons to about 1500 Daltons) present inautistic samples versus controls can be detected at varying relativelevels, which provides information regarding the biochemical spectrum ofcompounds present in both diseased and healthy tissue. Such spectraprovide “biochemical fingerprints” comprising combinations ofmetabolites whose changes, together, can serve as specific indicators ofautism. As shown herein, certain metabolites, including those involvedin glutamate, cysteine, methionine and γ-amino butyric acid (GABA)metabolism can have a synergistic role on the cerebellar pathogenesis ofautism. Additionally, non-annotated or unreported metabolites were foundto be present in autistic brains in a statistically-significant mannerand also provide useful candidate biomarkers for autism and autismspectrum disorders. Metabolites identified herein provide sensitive andunique biochemical signatures of autism that are useful as diagnosticbiomarkers for the disorder.

This disclosure herein of these metabolic signatures of autism andautistic spectrum disorders is the first to apply metabolomics onpostmortem autistic brains and to provide a fingerprint of metabolitesand their changes, between autistic and non-autistic individuals. Thestudies provided herein identified metabolites that were significantlyaltered in the brains of autistic subjects as compared to non-autisticcontrols. In addition, subsets of metabolites that were commonly alteredin autistic tissues were identified. As set forth in more detail below,differentially-secreted metabolites having a molecular weight frombetween about 10 Daltons to about 1500 Daltons were identified by liquidchromatography electrospray ionization time-of-flight mass spectroscopy(LC-ESI-TOF-MS) and/or hydrophilic interaction chromatography (HILIC),although the skilled worker will recognize that these methods arenon-limiting and that other methods for detecting metabolites inbiofluids from autistic patients can be utilized in the practice of themethods of this invention. However, the skilled worker will alsorecognize that this modality of mass spectrometry as applied tometabolomic analysis of fresh frozen brain tissue was highly sensitive(e.g., resolution of 3 ppm) and capable of detecting metabolites at verylow abundances (i.e., micromolar to picomolar concentrations). Inaddition, other analytical chemistry platforms known in the art forpracticing metabolomic methods, such as nuclear magnetic resonance (NMR)are less sensitive than mass spectrometry, requiring larger amounts ofbiological samples to detect metabolites at significantly higherconcentrations (reviewed in Glish et al., 2003, Nat. Rev. Drug Discov.2:140-150); nevertheless, the invention expressly envisions using suchmethods, and other methods known in the art, under appropriatecircumstances.

Specific embodiments of this invention will become evident from thefollowing more detailed description of certain preferred embodiments andthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects and features of this invention will be betterunderstood from the following detailed description taken in conjunctionwith the drawings, wherein:

FIGS. 1 (A) and (B) are total ion chromatograms of all postmortem brainsamples. FIG. 1 (A) represents HILIC chromatography and FIG. 1 (B)represents C18 chromatography. Autistic samples are designated by graylines and non-autistic controls are designated by black lines.

FIG. 2 (A) and FIG. 2 (B) are ion chromatogram controls that verifiedthe chemical identities of test sample metabolites by comparative ionfragmentation pattern (FIG. 2 (A)) and retention time (FIG. 2 (B)) inthe presence of standard chemical controls.

FIG. 3 (A)-FIG. 3 (C) are extracted ion chromatograms (EICs) ofL-cystathionine, and FIG. 3 (D) is a plot of the relative abundance ofL-cystathionine in autistic and control brain samples. FIG. 3 (A) showsthe results of metabolomic analysis of fresh frozen postmortem brainsamples of autistic (gray lines) and non-autistic controls (black lines)showing a significant increase in the abundance of L-cystathionine(exact mass 222.0628) in the post vermis and lateral hemispheres ofautistic brains. FIG. 3 (B) shows EIC having the retention time of achemical standard. FIG. 3 (C) shows EIC of brain samples run at the sametime as chemical standard, which matched retention time of chemicalstandard shown in FIG. 3 (B). FIG. 3 (D) is a mean plot of the abundanceof L-cystathionine in autistic and control brain samples. An ANOVA onthe difference of the means was statistically significant (p=0.019).Error bars are standard error of the mean.

FIG. 4 (A)-FIG. 4 (D) are extracted ion chromatograms of L-cystathionineand N-acetylaspartylglutamic acid. Liquid chromatography electrosprayionization time-of-flight-mass spectrometry (LC-ESI-TOF-MS)/MSfragmentation of L-cystathionine in brain samples (FIG. 4 (A)) andchemical standards (FIG. 4 (B)) have matching fragmentation patterns,confirming the identity of L-cystathionine. The identity ofN-acetylaspartylglutamic acid was confirmed by a matching MS/MSfragmentation pattern of brain (FIG. 4 (C)) and chemical standard (FIG.4 (D)).

FIG. 5 (A)-FIG. 5 (C) are extracted ion chromatograms (EICs) of2-aminooctanoic acid, and FIG. 5 (D) is a plot of the relative abundanceof 2-aminooctanoic acid in autistic and control brain samples. FIG. 5(A) shows the results of metabolomic analysis of fresh frozen postmortembrain samples of autistic (gray lines) and non-autistic controls (blacklines), revealing a significant decrease in the abundance of2-aminooctanoic acid (exact mass 159.1253) in the post vermis andlateral hemispheres of autistic brains, as shown by extracted ionchromatograms (EICs). FIG. 5 (B) is an EIC showing the retention time ofa chemical standard. FIG. 5 (C) are EICs of brain samples run at sametime as chemical standard, which match retention time of control shownin FIG. 5 (B). FIG. 5 (D) is a mean plot of the abundance of2-aminooctanoic acid in autistic and control brain samples. An ANOVA onthe difference of the means was statistically significant (p=0.027).Error bars are standard error of the mean.

FIG. 6 (A)-FIG. 6 (C) are extracted ion chromatograms (EICs) ofN-acetylaspartylglutamic acid (NAAG), and FIG. 6 (D) is a plot of therelative abundance of N-acetylaspartylglutamic acid in autistic andcontrol brain samples. FIG. 6 (A) shows the results of metabolomicanalysis of fresh frozen postmortem brain samples of autistic (graylines) and non-autistic controls (black lines) ofN-acetylaspartylglutamic acid (exact mass 304.0909) in the post vermisand lateral hemispheres. FIG. 6 (B) is an EIC showing retention times ofchemical standards. FIG. 6 (C) are EICs of brain samples run at the sametime as the chemical standard, which matched retention time shown inFIG. 6 (B). FIG. 6 (D) is a mean plot of the abundance ofN-acetylaspartylglutamic acid in autistic and control brain samples. AnANOVA on the difference of the means was not statistically significantat p less than 0.05 (p=0.054), but was at a p-value less than 0.1. Errorbars are standard error of the mean.

FIG. 7 (A) and FIG. 7 (B) are extracted ion chromatograms (EICs) of yetto be identified metabolites. Multiple unknown compounds were observedto be significantly different between autistic and control brainsamples. The EICs of two such metabolites are shown for molecules withthe exact mass of 366.1426 (FIG. 7 (A)) and 422.2482 (FIG. 7 (B)).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

This invention is more particularly described below and the Examples setforth herein are intended as illustrative only, as numerousmodifications and variations therein will be apparent to those skilledin the art. As used in the description herein and throughout the claimsthat follow, the meaning of “a”, “an”, and “the” includes pluralreference unless the context clearly dictates otherwise. The terms usedin the specification generally have their ordinary meanings in the art,within the context of the invention, and in the specific context whereeach term is used. Some terms have been more specifically defined belowto provide additional guidance to the practitioner regarding thedescription of the invention.

Autism remains a poorly-understood disease, particularly with regard toits etiology and the significance of any detectable alteration in brainchemistry to its etiology. A large number of changes in brain chemistryhave been detected in autistic brains (all post-mortem), but without anyconsistent understanding of whether such changes are fundamentallyrelated to development or progression of the disease or produced as aconsequence thereof. The metabolomic signatures provided herein, on theother hand, reflect consistent differences in the autistic brainsstudied and identify particular metabolic pathways consistentlyassociated with the disease.

The metabolome, defined as the total dynamic set of cellular metabolitespresent in cells or their surrounding matrix, is a product of health ordisease/insult states. Metabolomics is particularly sensitive toenvironmental effects in comparison to other “omic” areas of study, suchas genomics and proteomics. Cellular metabolites include but are notlimited to sugars, organic acids, amino acids and fatty acids,particularly those species secreted, excreted or otherwise released fromcells (e.g., as the result, inter alia, of chemical or physical trauma),that participate in functional mechanisms of cellular response topathological or chemical insult. Such cellular metabolites, properlyappreciated, can serve as biomarkers of disease or toxic response andcan be detected in biological fluids (Soga et al., 2006, J Biol Chem281:16768-78; Zhao et al., 2006, Birth Defects Res A Clin Mol Teratol76:230-6). Within the “omics” sciences, it is postulated thatmetabolomics is the platform that is closest to the phenotype incomparison to other “omics,” as it measures the end products ofmetabolism (Dettmer et al., 2007, Mass Spectrom. Rev. 26:51-78).

A metabolomic signature (i.e., a population of cellular metabolites)differentially produced by autistic patient biofluids provides areliable diagnostic marker for detecting autism. Certain aspects of theinvention provide assays of cellular metabolites using physicalseparation methods, including but not limited to liquid chromatographyelectrospray ionization time-of-flight mass spectrometry (ESI-TOF), forexample. Metabolites can be identified using their exact molecular mass,as well as mass spectrometry fragmentation patterns of the metabolites.The sensitivity of applying such methods to detecting cellularmetabolites produced by autistic patients provides improvedidentification of autistic disorders compared with less robust methodsin the art, which have focused on psychological, developmental andbehavioral assessments rather than metabolic biochemistry. Using ametabolomics diagnostic approach is a more reliable indicator ofautistic phenotype versus gene specific diagnostics given the fact thatnot all changes in gene transcription correlate with a phenotype.

Additional embodiments of the invention include a metabolomic signatureunique to autism. In one embodiment, a metabolic signature comprisingone or more cellular metabolites, and in a particular embodiment, one ormore cellular metabolites of the serotonin, cysteine, tryptophan,methionine, glutamate or GABA metabolic pathways, is provided herein. Incertain embodiments, the metabolites comprising a metabolomic signatureset forth herein comprise one or more of N-acetylaspartylglutamic acid,L-cystathionine, 2-aminooctanoic acid, 5-hydroxylysine,vinylacetylglycine, proline betaine, caffeine,3-carboxy-1-hydroxypropylthiamine diphosphate, 3′-sialyllactosamine,3,4-dihydroxybenzylamine, dipalmitoyl-phosphatidylcholine, SAICAR((S)-2-[5-Amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate),glutamate, or GABA intermediates. (e.g., metabolites involved in theanabolism or catabolism of GABA such as L-glutamine, L-glutamate,alpha-ketogluratate, succinic acid semialdehyde, 4-aminobutyraldehyde,4-guanidinobutanoate, or L-ornithine

Methods and reagents for identifying and measuring cellular andparticularly biochemical effects of autism, are provided, includingmethods for diagnosing autism. The term “metabolite,” “cellularmetabolite” or the plural form, “cellular metabolites,” as used hereinrefers to any molecule or mass feature in the range of about 10 Daltonsto about 1500 Daltons secreted by a cell and present in a tissue sampleor biofluid. A cellular metabolite can include but is not limited to thefollowing types of molecules: acids, bases, lipids, sugars, glycosides,amines, organic acids, lipids, amino acids, oximes, esters, dipeptides,tripeptides, fatty acids, cholesterols, oxysterols, glycerols, steroids,and/or hormones. In one embodiment, the cellular metabolites can includebut are not limited to N-acetylaspartylglutamic acid, L-cystathionine,2-aminooctanoic acid, 5-hydroxylysine, vinylacetylglycine, prolinebetaine, caffeine, 3-carboxy-1-hydroxypropylthiamine diphosphate,3′-sialyllactosamine, 3,4-dihydroxybenzylamine,dipalmitoyl-phosphatidylcholine, SAICAR((S)-2-[5-Amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate),glutamate, or GABA intermediates

The phrases “identifying one or a plurality of cellular metabolites . .. differentially produced” and “differentially produces” as used hereininclude but are not limited to comparisons of cells or tissues fromautistic humans with cells or tissues from non-autistic humans.Detection or measurement of variations in metabolite populations or massfeatures between autistic and non-autistic control samples are includedin this definition. In a preferred embodiment, alterations in productionof various metabolites are measured by determining a profile of changesin metabolite molecules in autistic versus control samples.

The term “physical separation method” as used herein refers to anymethod known to those with skill in the art sufficient to detect aprofile of changes and differences in metabolites produced in the tissueor biofluid (e.g., lateral cerebellum, and post vermis brain,cerebrospinal fluid, blood, or plasma) of autistic humans according tothe methods of this invention. In a preferred embodiment, physicalseparation methods permit detection of cellular metabolites includingbut not limited to sugars, organic acids, amino acids, fatty acids,hormones, vitamins, and peptides, as well as ionic fragments thereof andother cellular metabolites (preferably having a molecular weight lessthan 3000 Daltons, more particularly between 10 and 1500 Daltons, andeven more particularly between 100 and 1000 Daltons). In certainembodiments, the physical separation method is liquidchromatography/electrospray ionization time of flight mass spectrometry(LC/ESI-TOF-MS) and/or hydrophilic interaction chromatography (HILIC),however it will be understood that cellular metabolites as set forthherein can be detected using alternative spectrometry methods or othermethods known in the art for analyzing these types of cellular compoundsin this size range.

Data for statistical analysis can be extracted from chromatograms (i.e.,spectra of mass signals) using statistical analysis packages such asAgilent Mass Hunter software (Product No. G3297AA, Agilent Technologies,Inc. Santa Clara, Calif.); it will be understood that alternativestatistical analysis methods can be used instead. Masses areadvantageously binned together if they are within 10 ppm and elutedwithin a 2 minutes retention time window. A binned mass can beconsidered to be the same molecule across different LC/ESI-TOF-MSanalyses (referred to herein as an “exact mass”) if the detected massesare within ±10 ppm. Binning of the data is required for statisticalanalysis and comparison of masses across experiments. If multiple peakswith the same mass at the same retention time within a single samplewere detected, for example, by Mass Hunter, they can be averaged toassist data analysis. Masses lacking a natural isotopic distribution orwith a signal-to-noise ratio of less than 3 are removed from the dataprior to analysis. The results from these assays provided relativevalues that were assessed according to annotated values within 20 ppmand provided a putative identity for the molecular weight detectedaccording to chemical databases. Thus, a mass shift within 20 ppm wasconsidered consistent with determining the identity of a specificannotated cellular metabolite known in the art due to differences inionization source and instrumentation, (e.g., between differentexperiments or using different instruments).

As used herein, a mass can be considered to be the same across separateLC/ESI-TOF-MS injections using an algorithm that first sorts the data bymass and retention time. After sorting, a compound can be consideredunique if it had a retention time difference of less than or equal tothree minutes and a mass difference less than or equal to a weightedformula (0.00002× mass). If a series of measurements from differentseparations fit this definition said measurements are considered toarise from separation of the same compound. If either the mass or theretention time is found to vary by more than the limits listed above,the mass is considered to be a different compound and given its ownunique designation.

Significance tests such as ANOVAs on the log base 2 can be used totransform abundance values of unique compounds present in autisticversus not-autistic samplea at each time point. A randomized completeblock design using the ANOVA model including diagnosis, experiments, anda residual term, can be expressed using the following formula:

Log₂(abundance_(tb))=diagnosis_(t)+section_(b)+error_(tb).

Missing data are omitted from the test, changing the degrees of freedom(rather than assuming the missing data were absent). This assumption wasmade because the extensive filtering performed by the Mass Huntersoftware can miss or filter certain peaks because they are below acertain abundance threshold and not zero. The ANOVA F-test wasconsidered significant if its p-value was less than 0.05. Fold changeswere calculated using the least squared means for a given time andtreatment.

The terms “metabolic signature” and “biomarker profile” as used hereinrefer to one or a plurality of metabolites identified by the inventivemethods. Metabolic signatures and biomarker profiles according to theinvention can provide a molecular “fingerprint” of autism spectraldisorders and identify one or preferably a population of cellularmetabolites significantly altered in individuals with autism spectraldisorders. In preferred embodiments, metabolic signatures or biomarkerprofiles can be used to diagnose autism in an individual.

The term “biomarker” as used herein refers to cellular metabolites thatexhibit significant alterations between diseased and controls. Inpreferred embodiments, biomarkers are identified as set forth above, bymethods including LC/ESI-TOF-MS. Metabolomic biomarkers were identifiedby their unique molecular mass and consistency, thus the actual identityof the underlying compound that corresponds to the biomarker is notrequired for the practice of this invention. Alternatively, certainbiomarkers can be identified by, for example, gene expression analysis,including real-time PCR, RT-PCR, Northern analysis, and in situhybridization, but these will not generally fall within the definitionof the term “cellular metabolites” as set forth herein.

Metabolite profiling as set forth herein was conducted on postmortemtissue as opposed to samples collected from live patients. Ultimately,biomarkers discovered in vivo are expected to be useful for analyzingsamples such as biofluids including for example, cerebrospinal fluid,blood, plasma, amniotic fluid and urine, i.e., complex mixtures ofextracellular biomolecules. This inventive methods are advantageous overinvasive procedures such as tissue biopsies because metabolites inbiofluids can be detected non-invasively (in contrast to intracellularcompounds). In addition, processing cellular supernatant for massspectrometry is more robust and less laborious than cellular extracts.However, cellular extracts from, for example, tissue biopsies or lysedcells, are encompassed in the methods of the invention. The terms“samples” or “biosamples” or “patient samples” include but are notlimited to cerebrospinal fluid, brain tissue, amniotic fluid, blood, orplasma.

The identification of differential metabolites in the organ that isprimarily affected in autism (brain) prior to biofluid analysis providesa targeted approach towards the development of novel diagnostics.Identifying specific biomarkers for autism in biofluids, such ascerebrospinal fluid, urine or plasma, without a defined set ofcandidates, would be particularly arduous. Biofluids are complexmixtures of systemic byproducts influenced by both endogenous andexogenous factors (genetics, diet, the environment). Using the methodsof the current invention, specific analytical chemistry protocols can beused for assessing biofluids for defined exact mass and retention timesof specific candidate biomarkers.

As set forth in more detail in the Examples below, analysis of fifteenfresh frozen samples from the Autism Tissue Program (ATP) (six autismand nine controls) using the methods described herein detectedstatistically-significant differences in the abundance of multiplemetabolites between postmortem autistic and non-autistic brain samples.Preliminary experiments using hydrophilic interaction chromatography(HILIC) followed by positive mode ESI-TOF ionization identified a totalof 98 metabolites that were differentially produced between autistic andnon-autistic brains. For example, 5-hydroxylysine (exact mass 162.0981)displayed significant changes (p<0.05). Parallel preliminary experimentsusing C18 chromatography followed by positive mode ESI-TOF ionization,generated 47 statistically significant metabolites (p<0.05). Thechemical identity of these metabolites can be definitively confirmed bytandem mass spectrometry (MS-MS).

Metabolites from fresh frozen postmortem brain were initially separatedby liquid chromatography (LC) and then ionized and detected byelectrospray ionization time of flight mass spectrometry (ESI-TOF-MS).This mass spectrometry modality was chosen because it is particularlysuited for metabolomics of fresh frozen brain tissue: it is highlysensitive, detecting small molecule metabolites at very low abundances(e.g., micromolar to picomolar concentrations) and providing highlyaccurate measurements of the exact mass of metabolites with a resolutionof 3 ppm. Other analytical chemistry platforms employed in metabolomics,such as NMR (nuclear magnetic resonance) are less sensitive than massspectrometry, requiring larger amounts of biological samples to detectmetabolites at significantly higher concentrations (reviewed in Glish etal., 2003, Nat. Rev. Drug Discov. 2:140-150). As set forth herein,metabolomics analysis of postmortem brain samples revealed a pluralityof biochemical differences between autistic and non-autistic brains. Theidentification of cellular metabolites differentially secreted byautistic brain provide biomarkers for diagnosing autism, as set forthwith particularity herein.

In certain embodiments, the invention provides the followingmetabolites, taken alone, as a population, or in any informativecombination, as biomarkers of autism: N-acetylaspartylglutamic acid,L-cystathionine, 2-aminooctanoic acid, 5-hydroxylysine,vinylacetylglycine, proline betaine, caffeine,3-carboxy-1-hydroxypropylthiamine diphosphate, 3′-sialyllactosamine,3,4-dihydroxybenzylamine, dipalmitoyl-phosphatidylcholine, SAICAR((S)-2-[5-Amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate),glutamate, or GABA intermediates.

Abnormalities in neurochemical pathways have been hypothesized as apotential basis for the pathogenesis and/or clinical manifestation ofautism. It has been speculated with varying amounts of supporting,generally anecdotal evidence that perturbations to serotonin,kynurenine, glutamate, cysteine, and methionine metabolism may play arole in autism. The methods provided herein and the biomarkers providedaccording to the practice of these methods in contrast, provides anexperimental evidentiary basis for associating detection of one or aplurality of said biomarkers with the existence of autism or an autismspectrum disorder in an individual.

EXAMPLES

The Examples that follow are illustrative of specific embodiments of theinvention, and various uses thereof. They are set forth for explanatorypurposes only, and are not to be taken as limiting the invention.

Example 1 Preparation of Postmortem Brain Tissue

Postmortem brain tissue from autistic patients and age-matched controlswas obtained from the Autism Tissue Program (ATP). Altogether, 6autistic and 9 non-autistic postmortem brain samples from the Universityof Maryland Brain and Tissue Bank for Developmental Disorders werestudied (See Table 1). Metabolomics was performed with fresh frozensections of the lateral cerebellum and post vermis from theabove-mentioned samples. The cerebellum was selected because it is aregion where abnormal neuroanatomical findings most consistent inautistic patients are localized (Kemper et al., 1998, J. Neuropathol.Exp. Neurol. 57:645-652). Differences in age, PMI (postmortem interval)and degree of clinical manifestation were acknowledged to be a source ofvariability in the abundance and nature of metabolites measured bymetabolomics.

Prior to metabolomics, frozen brain specimens were mounted on thepre-cooled stage of a Leica SM200R microtome using 30% sucrose andsectioned to create uniform samples weighing ˜50 mg. Metabolites wereextracted in 80% methanol/20% 0.5% formic acid on a volume per wettissue weight ratio by homogenizing the sample in a conical glasshomogenizer. The samples were then stored at −20° C. for 30 minutes toprecipitate proteins and insoluble material. Thereafter, the coldincubation samples were centrifuged at 16,000×g for 25 minutes at 4° C.to pellet cell debris, proteins, and other insoluble material.Supernatent was then transferred to a new tube, and re-extracted with300 μl of extraction buffer. Samples were dried in a rotary evaporatorfor 6-7 hours until dry to remove the extraction buffer. Extracts werethen dissolved in 500 μl of 20% acetonitrile/80% 0.1% formic acid andcentrifuged through a Millipore 3 kDa Microcon column (Millipore) forthree hours at 13,000×g to remove large molecular weight (>3 kDa)biomolecules. Flowthrough was dried in a rotary evaporator and dissolvedin 2 μl/mg 95% 0.1% formic acid/5% acetonitrile and stored at −80° C.prior to analysis by LC-MS.

Liquid chromatography electrospray-ionization time-of-flight massspectrometry (LC-ESI-TOF-MS) was performed on these extracts using anAgilent QTOF LC/MS system consisting of a G6520AA QTOF high resolutionmass spectrometer capable of exact mass MS and MS/MS ion fragmentation.The polar metabolite fraction of each sample was analyzed using twodifferent chromatographic methods for maximal separation and smallmolecule resolution: a) 3×100 mm Phenomenex 3 μm Luna hydrophilicinteraction chromatography (HILIC) and b) 2.1×50 mm Zorbax 1.8 μm C18-SBcolumns (C18). This approach maximized the number of metabolites thatwere separated and subsequently ionized and measured. Ultra highpressure liquid chromatography (HPLC) analysis was performed with theZorbax C18 column using a 15 minute gradient from 5% acetonitrile/95%water/0.1% formic acid to 100% acetonitrile/0.1% formic acid at a flowrate of 400 μl/min. The HILIC HPLC method was performed using a 16minute gradient from 95% acetonitrile/5% water/0.1% formic acid to 60%acetonitrile/40% water/0.1% formic acid with a 500 μl/min flow rate.Electrospray ionization was employed using a dual ESI source, with anAgilent isocratic pump continuously delivering an internal massreference solution into the source at approx. 0.01 ml/min. The massrange of the instrument was set at 75-1500 Da and data were acquired in2 GHz mode to maximize dynamic range. Data acquisition was performedwith Agilent MassHunter version B.02.00 using high-resolution exact massconditions.

Example 2 Identification of Metabolites Differentially Produced inAutistic Brain

Prior to data analysis, total ion chromatogram (TIC) of each sample wascarefully inspected for quality and reproducibility of the MS signal.For those samples wherein the TIC abundance deviated by more than 25%from the median across the LC-MS gradient, LC-MS analysis was repeated(FIG. 1). Data was deisotoped and then converted into the open sourcemzData format.

Data analysis was performed using the open source statisticalprogramming and analysis software R. The XCMS package (Smith et al.,2006, Anal. Chem. 78:779-787) was used to analyze the LC-ESI-TOF-MSresulting files using the Centwave algorithm for peak peaking(Tautenhahn et al., 2008, BMC Bioinf. 9:504). Retention time deviationsacross LC-MS samples were corrected using rector (retention timecorrection) loess regression of features common to all LC-MS samples andthe features grouped using the density based functions in XCMS. Themaximum observed deviation for HILIC chromatography was 15 seconds and 4seconds for C18 chromatography. After the grouping function wasperformed features missing in LC-MS samples were iteratively integratedusing m/z and retention time windows based on the range of the featuregroup. The peak intensity tables were evaluated using both univariateand multivariate statistical analysis. Contaminant ions were removed bycomparing brain tissue extracts to mock extraction blanks Featurespresent in both the brain tissue extracts and the mock extraction blankswere removed from the data set if their abundance was less thanfive-fold greater than the extraction blank. Statistical significance ofindividual mass features was performed under the null hypothesis that nodifference in their abundance existed between control and autistic postmortem brains. Differential metabolites, or features, were determinedusing an incomplete block design ANOVA with the model log2(abundance)˜section+diagnosis on the combined brain regions. Featureswere considered statistically significant if they exhibited a foldchange greater than 25% and a p value less than 0.05 in the diagnosisfactor of the ANOVA model. The extracted ion chromatogram of eachstatistically significant feature was further evaluated to confirm anobservable difference between autistic and normal extracts to reduce theinclusion of spurious results.

Direct injections were analyzed using the Continuous Wavelet Transform(Du et al., 2006, Bioinformatics, 22:2059-2065) peak picking algorithmin XCMS. Agilent Mass Qualitative Analysis Software Version 2.00B wasused to convert vendor specific raw data files into open source mzDatafiles. Features with a height of equal to or greater than 1000 wereincluded for analysis and then deisotoped prior to identifying massfeatures. The generation of mass features (putative metabolites) wasimplemented using methods present in the XCMS (Smith et al., 2006,Anal., Chem. 78:779-787) library from R/Bioconductor. Each ESI polaritywas processed independently. The centwave algorithm (Tautenhahn et al.,2008, BMC Bioinf. 9:504) designed for the detection of peaks in highresolution high mass accuracy data was utilized for peak picking Theinput variables for the centwave algorithm were based on QTOF settingsand the chromatography method. Following peak picking deviations inretention times were corrected using loess regression. Mass feature binsor groups were generated using the density based grouping algorithmusing parameters optimized for LC-MS gradients. After data had beengrouped into mass features, missing features were integrated based onretention time and mass range of a feature using the iterative peakfilling method in XCMS.

Binning of mass features detected in individual LC-MS was used toidentify features that were present at the same retention time acrossinjections. A binning algorithm based on both exact mass and retentiontime was used to consider a mass feature to be the same across differentLC/ESI-MS-QTOF experiments. Binning criteria were based on both asliding mass difference scale that allowed for larger mass differencesat lower molecular weights and a constant retention time window based onthe reproducibility of the chromatography.

Statistical significance testing of individual mass features wasperformed under the null hypothesis that no difference in theirabundance existed between control and autistic vermis samples using apermutation based test statistic. This analysis was performed byexploring different combinations of metadata in the loading and scoresplots using the pcaMethods library in R (Stacklies et al., 2007.PcaMethods: A collection of PCA methods. R package version 1.12.0.Available from CRAN.R-project.org/). General differences between controland autism postmortem brain samples were evaluated using Welch t-tests.Bayesian principal component analysis was performed because this methodcan tolerate missing data (Oba et al., 2003, Bioinformatics,19:2088-2096) when determining whether global changes in metabolismexisted between control and autistic brain tissue. This analysis wasperformed by exploring different combinations of metadata in the loadingand scores plots using the pcaMethods library in R (Stacklies et al.,2007. Pca Methods: A collection of PCA methods. R package version1.12.0. Available from CRAN.R-project.org/).

Statistically significant features were also modeled using PrincipalCluster Analysis (PCA)-based methods and hierarchical clustering toexamine their ability to classify the samples in an unsupervised manner.As a result, a comprehensive list of low metabolites that werestatistically significantly different between postmortem autistic brainsand non-autistic controls were identified following statistical analysisof an average of 6,000 features per experiment. Mass spectrometry basedmetabolomic profiling, or metabolomics, revealed significant changes(p-value<0.05) in autistic brains in 98 metabolites using HILIC and 47metabolites using C18 chromatography.

TABLE 1 Autism Tissue Program (ATP) approved postmortem samples subjectto LC-ESI-TOF-MS metabolomics for identifying metabolites and pathwaysthat are statistically significantly altered in autistic brain. FrozenLat- Ver- eral Diagno- Age PMI UMB# mis Hem sis Sex Yrs Days Age (Hours)1182 + + Autism F 9 354 9.97 24 1185 + + Control M 4 258 4.71 171349 + + Autism M 5 220 5.60 39 1407 + − Control F 9 46 9.13 20 1500 + +Control M 6 320 6.88 18 1541 + + Control F 20 228 20.62 19 1638 + +Autism F 20 277 20.76 50 1674 + + Control M 8 339 8.93 36 1706 + +Control F 8 214 8.59 20 1708 + + Control F 8 50 8.14 20 1860 − + ControlM 8 2 8.01 5 4231 + + Autism M 8 300 8.82 12 4671 + + Autism F 4 1654.45 13 4721 + + Autism M 8 304 8.83 16 4898 + + Control M 7 272 7.75 1215 14 14 Autism = 8 Male 6 Control = 7 Female 9.19 18.56 9 UMB#represents archival sample number.

Example 3 Chemical Annotation of Autism-Specific Metabolites

Following identification of metabolites differentially secreted inautistic cerebelli as described in Example 2, chemical annotation ofthese molecules was further refined by ion fragmentation patternanalysis (MS-MS). Specifically, fragmentation patterns and retentiontimes of each small molecule identified above were compared toanalytical grade chemical standards. While confirmation of the chemicalidentity of each small molecule is not required for autistic biomarkerdetermination, annotated identity is advantageous in that it providesmore uniform nomenclature for each small molecule biomarker. Subsequentchemical identification of the differentially produced metabolites alsopermits in silico mapping of metabolites onto specific pathways viabioinformatics (GeneGo software). This information thus revealedsystematic metabolic pathways and/or networks that underlie biochemicaldifferences between postmortem brains of autistic and non-autistic agematched controls.

The neutral exact mass of each compound was queried against the publicsearchable databases METLIN (metlin.scripps.edu), The Human MetabolomeDatabase (www.hmdb.ca), and the Kyoto Encyclopedia of Genes and Genomes(www.genome.jp/kegg/), or the Biological Magnetic Resonance Bank(www.bmrb.wisc.edu/metabolomics/) for candidate identities. Measuredmass features are considered to match a metabolite present in thedatabases if their exact masses were within 20 parts per million of theannotated database molecule (0.00002× mass). The chemical identity ofthe biomarker candidates was confirmed using one or more of thefollowing three criteria: (1) molecular exact mass, (2) MS/MSfragmentation pattern and (3) chromatographic retention time. The exactmass of each candidate biomarker (i.e., mass feature), as determined byinterpretation of electrospray ionization time of flight massspectrometry (LC-ESI-TOF MS/MS) (i.e., tandem mass spectrometry) datawas queried against the various public searchable databases set forthabove for putative molecular identities Annotated candidates were thenvalidated by acquiring MS/MS product ion spectra for the candidatemolecular ion, then matching the MS/MS fragmentation pattern for production spectra obtained for a known analytical grade standard referencecompound (Cummings et al., 2009, J Chromatogr B Analyt Technol BiomedLife Sci., 877:1221).

Analytical grade chemical standards for L-cystathionine, 2-aminooctanoicacid and N-acetylaspartylglutamic acid were purchased from SigmaChemical Co., St. Louis, Mo. for comparative mass spectrometry. Chemicalreferences were evaluated using identical chromatographic methods usedin the metabolomic analysis of the original samples. Additionally, sevenof the original samples (4 autistic and 3 control) were re-analyzed forcomparison with the chemical standards, to ensure retention time and ionfragmentation match. Chemical references were dissolved in appropriatebuffers and 100 nM standards were prepared by diluting into 95% 0.1%formic acid/5% acetonitrile. The experimental samples and chemicalstandards were fragmented using the following formula to determinecollision energy: Collision Energy=(m/z of precursor-100)/100*3V+10V. Aputative annotation was considered correct if the retention time andfragmentation pattern in the original sample matched the chemicalreferences. If the putative metabolite was insufficiently abundant inthe sample extract to be fragmented, the retention time alone was usedto confirm annotation.

The third confirmation criterion was established by co-injecting (i.e.,spiking) endogenous metabolites with a known reference compound todetermine whether the chromatographic retention time and ionizationfragmentation patterns (FIG. 2) of the chemical standard matched theretention time of the biomarker candidate. Tandem mass spectrometry, orMS-MS, was used to confirm the identity of an endogenous metabolite inthese experiments. After data analysis was completed, a subset ofstatistically significant metabolites was selected for confirmationagainst known chemical standards.

In these assays, fresh frozen brain extracts used to identify massfeatures as discussed above were spiked with 1 mM of the chemicalstandards and then analyzed by MS-MS. Remaining unused samples or spikedsamples were processed by LC-ESI-MS/MS to confirm the structure andabundance of the ions of the putatively-identified compounds. Standardsused as controls in these experiments had known retention times.Parallel retention times observed in spiked samples provided additionalconfirmation of metabolite identity. Final confirmation of chemicalidentity of candidate biomarkers was made upon both detection offragment ions (FIG. 2, A) and retention time (FIG. 2, B). Classificationof hits was based on a “shared peak count” method that identified massesof fragment ions shared between the analytical (fresh frozen brainextract) and chemical reference MS/MS spectra.

Statistically significant differences were detected in the abundance ofmultiple metabolites between postmortem autistic and non-autistic brainsamples (Table 2). To focus on meatbolites that have a high probabilityof being significant, and to eliminate potential spurious artifacts,each data set (HILIC and C18 chromatography) was filtered using amoderate filter of a 25% fold change in abundance between control andautistic brain samples. HILIC chromatography followed by positive modeESI-QTOF ionization identified 988 mass features exhibiting a foldchange greater than 25%, of which 98 metabolites were statisticallysignificantly different (p<0.05) between autistic and non-autisticbrains. Additionally, C18 chromatography followed by positive modeESI-QTOF ionization measured 938 mass features with at least a 25% foldchange, generating 47 statistically significant metabolites (p<0.05).Table 2 shows a subset of the measured significant metabolites that haveat least a 25% change in abundance between autistic and controlpostmortem brain samples with a corresponding extracted ion chromatogram(EIC) displaying a marked difference between autistic and control brainsamples. One of the most challenging aspects of metabolomics studies isthat many of the small molecules measured are yet not annotated inpublic databases. While current methods allow the identification ofsmall molecule metabolites by unique mass, the identification bycompound name is more challenging. The metabolites identified representnovel biomarkers, and importantly point to additional pathwayscontributing to the etiology of autism.

TABLE 2 Metabolites that exhibited significantly different levelsbetween postmortem autistic and non-autistic brain samples. AlterationExact Retention in autistic Neutral time brain Biochemical Mass(minutes) samples Putative Annotation Pathway p-value C18 Chromatography136.5265 1.48 ↑ Unspecified 0.049 143.0939 0.33 ↓ Vinylacetylglycine/Homocysteine 0.01 Proline Betaine 159.1253 2.72 ↓ DL-2-Aminooctanoic0.02 acid (Confirmed) 173.0308 0.33 ↓ Unspecified 0.04 180.0640 0.93 ↓Unspecified 0.0005 194.0805 3.62 ↓ Caffeine 0.034 279.1636 0.4 ↓Unspecified 0.03 286.1751 2.06 ↑ Unspecified 0.012 321.0561 1.5 ↑Unspecified 0.02 356.2055 3.16 ↑ Unspecified 0.05 363.1455 1.33 ↑Unspecified 0.05 366.1421 0.58 ↑ Unspecified 0.016 382.8678 1.22 ↑Unspecified 0.035 408.2181 5.58 ↓ Unspecified 0.029 422.2482 4.83 ↑Unspecified 0.023 426.1936 4.67 ↑ Unspecified 0.039 531.2841 1.14 ↑Unspecified 0.049 597.2905 6.98 ↓ Unspecified 0.012 599.3340 1.07 ↑Unspecified 0.032 632.2266 0.56 ↑ 3-carboxy-1- 0.037hydroxypropylthiamine diphosphate/3′- Sialyllactosamine 806.0834 5.18 ↓Unspecified 0.027 823.0550 6.85 ↑ Unspecified 0.016 823.7238 6.85 ↑Unspecified 0.034 HILIC Chromatography 87.0139 8.74 ↑ 3,4- 0.02Dihydroxybenzylamine 133.0168 8.74 ↑ Unspecified 0.008 133.0195 8.98 ↑Unspecified 0.016 139.0609 1.33 ↓ Unspecified 0.03 143.0922 2.55 ↓Unspecified 0.049 162.0981 9.37 ↑ 5-hydroxylysine 0.008 222.0628 8.74 ↑L-Cystathionine Tryptophan 0.019 (Confirmed) 241.1764 5.76 ↓ Unspecified0.019 255.0853 5.16 ↑ Unspecified 0.039 304.0909 1.23 ↑ N- Neuropeptide0.054 Acetylaspartylglutamic acid (Confirmed) 309.9921 5.68 ↓Unspecified 0.046 317.1924 5.87 ↑ Unspecified 0.002 364.0987 4.07 ↑Unspecified 0.042 418.0277 3.37 ↑ Unspecified 0.018 430.1120 7.66 ↑Unspecified 0.047 438.0307 2.58 ↑ Unspecified 0.013 454.0045 2.6 ↑Unspecified 0.02 454.0692 4.04 ↑ SAICAR 0.042 464.0335 3.36 ↑Unspecified 0.018 473.2109 4.06 ↑ Unspecified 0.025 501.9891 3.34 ↑Unspecified 0.012 517.1560 4.09 ↑ Unspecified 0.027 522.0543 4.02 ↑Unspecified 0.012 527.0686 2.63 ↑ 3-carboxy-1- 0.014hydroxypropylthiamine diphosphate 538.0278 3.96 ↑ Unspecified 0.007543.0427 2.89 ↑ Unspecified 0.017 547.9951 3.41 ↑ Unspecified 0.008568.0602 4.03 ↑ Unspecified 0.004 585.1485 4.10 ↑ Unspecified 0.035590.0422 4.02 ↑ Unspecified 0.001 616.9752 2.61 ↑ Unspecified 0.015636.0475 4.07 ↑ Unspecified 0.032 645.1668 7.67 ↑ Unspecified 0.041658.0298 4.06 ↑ Unspecified 0.031 668.0565 2.79 ↑ Unspecified 0.044674.0040 4.01 ↑ Unspecified 0.015 733.5650 0.79 ↓ Dipalmitoyl- 0.044phosphatidylcholine 811.0869 4.15 ↑ Unspecified 0.038 860.2212 7.68 ↑Unspecified 0.042 882.1976 7.67 ↑ Unspecified 0.049

Chemical Annotation of Differentially Secreted Metabolites

Chemical annotations were confirmed for the following subset ofdifferentially secreted metabolites.

L-cystathionine

Several metabolites in the cysteine and methionine metabolic pathwaywere significantly elevated in autistic brains in comparison tocontrols. Using HILIC chromatography, a statistically significantdifference was detected for L-cystathionine (exact mass 222.067) in boththe post vermis and lateral cerebellum of autistic brain samples(p=0.019, FIGS. 3(A) and 3(D)). The chemical identity of L-cystathioninewas secondarily confirmed by comparison of the retention time (FIGS.3(B) and 3(C)) and ion fragmentation pattern between the postmortembrain samples (FIG. 4(A)) and the chemical standard (FIG. 4(B)).

2-Aminooctanoic Acid

In contrast to L-cystathionine, the abundance of the metabolite2-aminooctanoic acid (exact mass 159.125) was significantly reduced inboth the post vermis and lateral cerebellum of autistic patients(p=0.027, FIGS. 5(A) and 5(D)). The global brain decrease in2-aminooctanoic acid was 144% between autistic and control samples.2-aminooctanoic acid has not been previously studied in brain samples,and thus the first evidence that alterations in 2-aminooctanoic acidconcentration is related to autism is provided herein. Previous studieshave only reported and measured this compound in urine (Parry et al.,1957, Clinica Chimica Acta, 2:115-125). Comparative mass spectrometrybetween the chemical standard (FIG. 5(B)) and postmortem brain samples(FIG. 5(C)) demonstrated a match in metabolite retention time. The lowabundance range of 2-aminooctanioc acid in postmortem brain samples wasan impediment to obtaining its ion fragmentation according to thestandard MS-MS method employed for other metabolites in this study.

N-Acetylaspartylglutamic Acid (NAAG)

The abundance of N-acetylaspartylglutamic acid (exact mass 304.091) waselevated in post-mortem autistic brains in comparison to normalcontrols. These results are consistent with previously reportedperturbations in the glutamate pathway for a human in vitro model ofautism (Cezar et al., 2007, Stem Cells Dev. 16:869-882), as well asother clinical studies (Blaylock et al., 2009, Curr. Med. Chem.16:157-170; Friedman et al., 2006, Arch. Gen. Psychiatry, 63:786-794;Kleinhans et al., 2007, Brain Res. 1162, 85-97; Pardo et al., 2007,Brain Pathol. 17:434-447; Shinohe et al., 2006, Prog.Neuro-Psychopharmacol. Biol. Psychiatry, 30:1472-1477). However, unlikeprevious reports, the results set forth herein show a previouslyunappreciated direct perturbation to the glutamate metabolismintermediate, N-acetylaspartylglutamic acid. Showing statisticalsignificance (p=0.054, FIGS. 6(A) and 6(D)), the chemical identity ofN-acetylaspartylglutamic acid was confirmed by comparative massspectrometry, revealing both a retention time (FIGS. 6(B) and 6(C)) andion fragmentation pattern between the postmortem brain samples (FIG.4(C)) and the chemical standard (FIG. 4(D)).

Unspecified Compounds

In addition to those metabolites described above, the metabolomicsanalyses described herein identified alterations in multiplenon-annotated metabolites (i.e., not annotated in public databases).Abundance of these metabolites was significantly increased or decreasedin autistic brain samples as compared to controls (p<0.05). Theextracted ion chromatograms of two such unspecified small moleculemetabolites are shown in FIGS. 7(A) and 7(B) (exact masses 366.1426 and422.2482, respectively). Both of these non-annotated endogenousmetabolites were significantly increased in autistic brain regions.Feature A (exact mass 366.1426) had a 42% increase in abundance betweenautistic and control brain samples. The abundance of feature B (exactmass 422.2482) was increased 181% in autistic brain samples. Lack ofcurrent annotation of these metabolites in public databases does notexclude them as candidate diagnostic biomarkers for autism. The highlysensitive, quantitative nature of the analytical chemistry detectionsystem employed herein provides a measurable endpoint (i.e., exactmass), which can be measured in patient samples (e.g., tissues orbiofluids) from autistic patients. In addition, other analyticalplatforms, sch as NMR, can be used to confirm the chemical formula ofthese compounds. An absence in chemical annotation merely preventsmapping of the molecules onto a specific biochemical pathway.

In summary, statistically significant differences were detected in theabundance of multiple metabolites ranging in size from about 10 to about1500 Daltons between postmortem autistic and non-autistic brain samples(Table 2). HILIC chromatography followed by positive mode ESI-QTOFionization identified a total of 98 metholites that were statisticallysignificantly different (p<0.05) between autistic brain and non-autisticcontrols. Additionally, C18 chromatography followed by positive modeESI-QTOF ionization, generated 47 statistically significant metabolites(p<0.05). This comprehensive signature provides immediate means fortranslational approaches to examine if perturbations to these endogenouschemicals are also measured in a quantitative manner in biofluids.

Example 4 Identification and Assessment of Metabolites Secreted inPatient Biofluids

While the assessment of brain tissue, the organ primarily affected byautism, permits the identification of autistic biomarkers as describedherein, the inventive methods are not limited to the examination ofbrain tissue. Analyzing patient samples such as biofluids, including forexample, cerebrospinal fluid, blood, plasma, amniotic fluid and urine,i.e., complex mixtures of extracellular biomolecules will also enablethe identification and assessment of differentially secretedmetabolites. This method is advantageous over invasive procedures suchas tissue biopsies because small molecules present in biofluids can bedetected non-invasively (in contrast to intracellular compounds). Inaddition, processing cellular supernatant for mass spectrometry is morerobust and less laborious than cellular extracts. Cellular extractsfrom, for example, tissue biopsies or lysed cells, are encompassed inthe methods of the invention.

Furthermore, one of skill would appreciate the secretion of brainmetabolites into surrounding cerebrospinal fluid. In turn, thecollection and assessment of cerebrospinal fluid by the describedmethods (i.e., metabolomics analysis) will permit the analysis andidentification of differentially expressed metabolites. Using themethods of the current invention, specific analytical chemistryprotocols can be used for assessing cerebrospinal fluid and otherbiofluids for defined exact mass and retention times of specificcandidate biomarkers.

Thus, in these aspects of the inventive methods a biofluid is obtained,such as cerebrospinal fluid, using conventional techniques (e.g., lumbarpuncture) and the biofluid processed as set forth herein to removeproteins and other biological materials having a molecular weightgreater than about 3000 Daltons. Separation methods, for example LC-TOFmass spectrometry, are applied to the processes biofluid and metabolitesdetected therein. Detection of one or a plurality of differentiallyproduced metabolites characteristic for autism is used to identify asubject having or at risk for autism, and to indicate that furtherbehavioral and other diagnostic methods are indicated.

Similarly, one of skill would appreciate the secretion of brain smallmetabolites across the blood-brain barrier. Metabolomic analysis ofblood plasma has been performed on blood samples obtained from mammalsas described: Thaw plasma and add 450 ul of ice cold MeOH:H₂O (8:1 v/v)and agitate for 10 minutes at 2-8 C. Then centrifuge the samples at18,400×g for 20 minutes at 4 C and transfer the supernatant to a tubeand repeat steps 3 and 4 until no pellet is observed. Dry samples andthen re-solublize in 50 ul of 50:50 ACN: 0.1% formic acid for subsequentmetabolic analysis. In summary, the inventive methods are applied tobiofluids including cerebrospinal fluid and plasma as two examples.

Example 5 Identification of Biochemical and Metabolic Pathways Alteredin Autistic Brain

Statistically significant metabolites whose chemical identity wasconfirmed as described above were compared and mapped in silico to theKyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. AFisher's exact test was used find biochemical pathways that werestatistically overrepresented. Groupings of features that show a commontrend across samples also were evaluated using the KEGG database todetermine if any biochemical interactions exist between the moleculesthat are not represented on a canonical KEGG pathway. This approachallowed mechanistic pathways that underlie metabolic changes observed invivo to be identified. Mapping differential metabolomic profiles totheir respective biochemical pathways as outlined in the KyotoEncyclopedia of Genes and Genomes (KEGG, release 41.1,www.genome.jp/kegg) revealed specific metabolic pathways that wereupregulated or downregulated in the cerebellum of autistic subjects incomparison to non-autistic controls. Significant changes to glutamate,cysteine, methionine, tryptophan and GABA metabolic pathways weredetected in autistic brains in comparison to non-autistic controls.

The results of the metabolomic methods set forth herein measuredsimultaneous changes in multiple biochemical pathways and networks,providing a quantitative de facto biochemical signature of specificautistic brain regions. Comparative metabolomics between postmortembrains of autistic patients and non-autistic controls revealed robustbiomarkers as well as biochemical changes that provide means for theearly diagnosis of autism. The differentially secreted metabolitesprovided herein comprise several candidate diagnostic biomarkers forautism.

The invention is not intended to be limited to the disclosed embodimentsof the invention. It should be understood that the foregoing disclosureemphasizes certain specific embodiments of the invention and that allmodifications or alternatives equivalent thereto are within the spiritand scope of the invention as set forth in the appended claims.

1. A method for identifying a metabolomic signature characteristic forautism in a human, the method comprising the steps of: a. assayingbiosamples isolated from autistic patients for one or a plurality ofcellular metabolites having a molecular weight of from about 10 Daltonsto about 1500 Daltons; b. assaying biosamples isolated from non-autisticpatients for one or a plurality of cellular metabolites having amolecular weight of from about 10 Daltons to about 1500 Dalton, whereinsteps a and b further comprise separating members of the population ofcellular metabolites that are present in said biosamples, and detectingone or a plurality of differentially produced cellular metaboliteshaving a molecular weight from about 10 to about 1500 Daltons; and c.selecting one or a plurality of metabolites having a molecular weightfrom about 10 Daltons to about 1500 Daltons that are differentiallyproduced in autistic patients, wherein said one or a plurality ofdifferentially produced metabolites comprise a metabolomic signature forautism.
 2. The method of claim 1, wherein the cellular metabolites areassayed using a physical separation method.
 3. The method of claim 2,wherein the physical separation method is liquid chromatographyelectrospray-ionization time of flight mass spectrometry.
 4. The methodof claim 1, further comprising the step of determining a chemicalidentity for one or a plurality of the cellular metabolites.
 5. Themethod of claim 4, wherein the chemical identity of one or a pluralityof the cellular metabolites is determined using molecular exact mass forthe metabolite or mass spectrometry fragmentation patterns of themetabolites.
 6. The method of claim 1, wherein the biosample iscerebrospinal fluid, brain tissue, amniotic fluid, or plasma.
 7. Amethod according to claim 1, wherein the cellular metabolites comprisecompounds from cellular metabolic pathways for serotonin, cysteine,methionine, glutamate, homocysteine, tryptophan, or gamma-aminobutyricacid (GABA).
 8. A method according to claim 1, wherein the cellularmetabolites are N-acetylaspartylglutamic acid, L-cystathionine,2-aminooctanoic acid, 5-hydroxylysine, vinylacetylglycine, prolinebetaine, caffeine, 3-carboxy-1-hydroxypropylthiamine diphosphate,3′-sialyllactosamine, 3,4-dihydroxybenzylamine,dipalmitoyl-phosphatidylcholine, or SAICAR((S)-2-[5-Amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate).9. A metabolomic signature for autism produced according to the methodof claim
 1. 10. A metabolomic signature according to claim 9, whereinthe cellular metabolites are N-acetylaspartylglutamic acid,L-cystathionine, 2-aminooctanoic acid, 5-hydroxylysine,vinylacetylglycine, proline betaine, caffeine,3-carboxy-1-hydroxypropylthiamine diphosphate, 3′-Sialyllactosamine,3,4-dihydroxybenzylamine, dipalmitoyl-phosphatidylcholine, or SAICAR((S)-2-[5-Amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate).11. A method for diagnosing autism in a human test subject, the methodcomprising the steps of: a. assaying a patient sample from theindividual for one or a plurality of cellular metabolites having amolecular weight of from about 10 Daltons to about 1500 Daltons; and b.detecting one or a plurality cellular metabolites comprising ametabolomic signature for autism that are differentially producedcompared to control in a patient sample.
 12. The method of claim 11,wherein the cellular metabolites are assayed using a physical separationmethod.
 13. The method of claim 12, wherein the physical separationmethod is liquid chromatography electrospray-ionization time of flightmass spectrometry.
 14. The method of claim 11, wherein the patientsample is cerebrospinal fluid, brain tissue, or plasma.
 15. A methodaccording to claim 11, wherein the cellular metabolites comprisecompounds from cellular metabolic pathways for serotonin, cysteine,methionine, homocysteine, tryptophan, glutamate, or GABA.
 16. A methodaccording to claim 11, wherein the cellular metabolites areN-acetylaspartylglutamic acid, L-cystathionine, 2-aminooctanoic acid,5-hydroxylysine, vinylacetylglycine, proline betaine, caffeine,3-carboxy-1-hydroxypropylthiamine diphosphate, 3′-sialyllactosamine,3,4-dihydroxybenzylamine, dipalmitoyl-phosphatidylcholine, or SAICAR((S)-2-[5-Amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate).17. A method for diagnosing autism in a human fetus in utero, the methodcomprising the steps of: a. assaying a fetal sample for one or aplurality of cellular metabolites comprising a metabolomic signature;and b. diagnosing the fetus with autism wherein the sampledifferentially produces one or a plurality of the cellular metabolitescomprising an autistic metabolomic signature.
 18. The method of claim17, wherein the metabolomic signature comprises one or a plurality ofcellular metabolites from serotonin, cysteine, methionine, homocysteine,tryptophan, glutamate or GABA metabolic pathways.
 19. The method ofclaim 17, wherein the metabolic signature comprises one or a pluralityof N-acetylaspartylglutamic acid, L-cystathionine, 2-aminooctanoic acid,5-hydroxylysine, vinylacetylglycine, proline betaine, caffeine,3-carboxy-1-hydroxypropylthiamine diphosphate, 3′-Sialyllactosamine,3,4-dihydroxybenzylamine, dipalmitoyl-phosphatidylcholine, or SAICAR((S)-2-[5-Amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate).20. A metabolic signature for autism, wherein the autistic metabolicsignature comprises one or a plurality of cellular metabolites having amolecular weight of from about 10 to about 1500 Daltons that aredifferentially produced in autism, wherein the plurality of cellularmetabolites comprise metabolites from the serotonin, cysteine,methionine, homocysteine, tryptophan, glutamate or GABA metabolicpathways.
 21. A metabolic signature of claim 20, wherein the metabolicsignature comprises one or a plurality of N-acetylaspartylglutamic acid,L-cystathionine, 2-aminooctanoic acid, 5-hydroxylysine,vinylacetylglycine, proline betaine, caffeine,3-carboxy-1-hydroxypropylthiamine diphosphate, 3′-sialyllactosamine,3,4-dihydroxybenzylamine, dipalmitoyl-phosphatidylcholine, or SAICAR((S)-2-[5-Amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido]succinate).22. A method for identifying a human with autism, the method comprisingthe steps of: assaying biosamples isolated from a human patients for oneor a plurality of cellular metabolites having a molecular weight of fromabout 10 Daltons to about 1500 Daltons by separating members of apopulation of cellular metabolites from said biosamples; whereindetection of said one or a plurality of metabolites having a molecularweight from about 10 Daltons to about 1500 Daltons that aredifferentially produced in autistic patients identifies the human ashaving autism.